AmharicIR+Instr: A Two-Dataset Resource for Neural Retrieval and Instruction Tuning
This provides a resource for researchers working on Amharic and other low-resource languages, enabling reproducible work in neural retrieval and generative modeling, but it is incremental as it applies existing methods to new data.
The authors tackled the scarcity of high-quality supervised data for low-resource languages like Amharic by releasing two datasets: one with 1,091 query-document triplets for neural retrieval-ranking and another with 6,285 prompt-response pairs for instruction-following text generation, both manually verified and formatted for reproducibility.
Neural retrieval and GPT-style generative models rely on large, high-quality supervised data, which is still scarce for low-resource languages such as Amharic. We release an Amharic data resource consisting of two datasets that supports research on (i) neural retrieval-ranking and (ii) instruction-following text generation. The retrieval-ranking dataset contains 1,091 manually verified query-positive-negative document triplets drawn from diverse Amharic sources and constructed to support contrastive training and benchmarking of neural retrievers (e.g., DPR, ColBERT-style late interaction and SPLADE-style sparse neural retrieval). Triplets are created through a combination of expert-curated queries, web-derived queries, and LLM-assisted generation, with positive/negative documents selected from the web or synthesized by LLMs and then validated by native speakers. The instruction prompt-response dataset comprises 6,285 Amharic prompt-response pairs spanning multiple domains and instruction types, generated with several LLMs and refined through manual review and correction for grammaticality, relevance, fluency, and factual plausibility. We release both datasets with standardized splits and formats (CSV,JSON,JSONL) to enable reproducible work on Amharic retrieval, ranking, and generative modelling. These datasets also come with a methodology that can be generalized to other low-resource languages.